Manufacturing organizations increasingly rely on data from multiple operational systems to manage procurement, production and logistics. Yet, workflows across these systems often remain fragmented, with employees depending on emails, spreadsheets and manual processes to transfer information.
Databricks is demonstrating how organizations can address this challenge by mirroring operational data into the lakehouse and using it as the foundation for transactional applications that connect different stages of the manufacturing value chain.
The demonstration, presented by Max Köhler, Lead Solutions Architect at Databricks, highlights how teams can bring procurement and production workflows together through a connected application environment.
Building Applications on the Lakehouse
The demonstration showcases a supplier portal, warehouse clerk workflow and manager dashboard assembled using Databricks technologies, including Lakebase, Databricks Apps, Genie and model serving.
The approach begins by mirroring data from existing operational systems into the lakehouse, creating a unified foundation for application development. Instead of relying on manual handoffs between systems, teams can build workflows directly around shared operational data.
For procurement teams, the supplier portal enables suppliers to book delivery slots, creating a more structured process for coordinating incoming materials. Once requests are submitted, warehouse clerks can review them through the application and confirm or reject delivery requests based on operational requirements.
Improving Visibility Across the Plant
The workflow also extends to management, with a dashboard designed to provide an aggregated view of delivery-slot utilization across the manufacturing plant.
This gives managers a clearer picture of capacity and scheduling while connecting information from procurement and warehouse operations within a single workflow. By reducing manual coordination, the approach aims to improve visibility and streamline operational decision-making.
Lakebase as the Transactional Layer
A key component of the demonstration is Lakebase, which serves as the transactional data layer behind the applications. The managed Postgres layer supports transactional reads and writes while providing capabilities such as database branches, autoscaling, and backup and restore options.
The demonstration illustrates how operational systems can be mirrored into the lakehouse and subsequently used to build applications that connect procurement, warehouse operations and management.
By combining lakehouse data with transactional application capabilities, Databricks is positioning Lakebase as a foundation for more connected and responsive manufacturing workflows.
Also Read :- Snowflake Holds Ground as Databricks Gains Momentum in AI and Data Race
